Add Text-Generation/Chat MaaP FineTuning sample

sdk/python/jobs/finetuning/standalone/finetuning-job-cliv2-readme.md

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+# Running a Fine-Tuning Job from CLI
+
+This guide provides instructions on how to run a fine-tuning job using the Azure Machine Learning CLI v2.
+
+## Prerequisites
+
+1. **Azure CLI**: Ensure you have the Azure CLI installed. If not, you can install it from [here](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli).
+
+2. **Azure Machine Learning CLI v2**: Install the Azure Machine Learning CLI extension v2.
+   ```bash
+   az extension add -n ml -y
+   ```
+
+3. **Azure Subscription**: Ensure you have an active Azure subscription and the necessary permissions to create and manage resources.
+
+4. **Resource Group and Workspace**: Ensure you have an Azure resource group and an Azure Machine Learning workspace. If not, you can create them using the following commands:
+
+      ```bash 
+      az group create --name <resource-group-name> --location <location>
+      az ml workspace create --name <workspace-name> --resource-group <resource-group-name> --location <location>
+      ```
+
+### Running the Fine-Tuning Job
+To run the fine-tuning job, use the following command:
+
+```bash
+az ml job create --file text-generation-finetuning-amlcompute.yaml --resource-group hyperdrive-service-static-rg --workspace-name train-finetune-dev-eastus --name "ft-maap-llama3-instance-types-1209-01"
+```
+
+#### Command Breakdown
+* az ml job create: Command to create and run a job in Azure Machine Learning.
+* --file text-generation-finetuning-amlcompute.yaml: Specifies the YAML file that defines the job configuration.
+* --resource-group hyperdrive-service-static-rg: Specifies the Azure resource group.
+* --workspace-name train-finetune-dev-eastus: Specifies the Azure Machine Learning workspace.
+* --name "ft-maap-llama3-instance-types-1209-01": Specifies the name of the job.
+
+Sample Yaml file for generating FineTuningJob using azureml CLIV2
+
+**Text Generation FineTuning**
+1. [finetuning-with-amlcompute](./model-as-a-platform/text-generation/text-generation-finetuning-amlcompute.yaml)
+2. [finetuning-with-instance-types](./model-as-a-platform/text-generation/text-generation-finetuning-instance-types.yaml)
+
+**ChatCompletion FineTuning**
+1. [finetuning-with-amlcompute](./model-as-a-platform/chat/chat-completion-finetuning-amlcompute.yaml)
+2. [finetuning-with-instance-types](./model-as-a-platform/chat/chat-completion-finetuning-instance-types.yaml)

sdk/python/jobs/finetuning/standalone/model-as-a-platform/chat/chat-completion-finetuning-amlcompute.yaml

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+type: finetuning
+
+name: "Phi-3-mini-4k-instruct-with-amlcompute"
+experiment_name: "Phi-3-mini-4k-instruct-finetuning-experiment"
+display_name: "Phi-3-mini-4k-instruct-display-name"
+task: chat_completion
+model_provider: custom
+model: 
+  path: "azureml://registries/azureml/models/Phi-3-mini-4k-instruct/versions/14"
+  type: mlflow_model
+compute: "lowpri-a100"
+training_data: train.jsonl
+validation_data:
+  path: validation.jsonl
+  type: uri_file
+hyperparameters:
+  num_train_epochs: "1"
+  per_device_train_batch_size: "1"
+  learning_rate: "0.00002"
+properties:
+  my_property: "my_value"
+tags:
+  foo_tag: "bar"
+outputs:
+  registered_model:
+    name: "Phi-3-mini-4k-instruct-finetuned-model"
+    type: mlflow_model

sdk/python/jobs/finetuning/standalone/model-as-a-platform/chat/chat-completion-finetuning-instance-types.yaml

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+type: finetuning
+
+name: "Phi-3-mini-4k-instruct-with-instance-types"
+experiment_name: "Phi-3-mini-4k-instruct-finetuning-experiment-instance-types"
+display_name: "Phi-3-mini-4k-instruct-display-name"
+task: chat_completion
+model_provider: custom
+model: 
+  path: "azureml://registries/azureml/models/Phi-3-mini-4k-instruct/versions/14"
+  type: mlflow_model
+resources:
+  instance_types:
+  # You can specify multiple instance types for the training job
+  # Backend will decide which instance type to use based on the constraints on training job.
+    - "Standard_NC96ads_A100_v4"
+    - "Standard_E4s_v3"
+training_data: train.jsonl
+validation_data:
+  path: validation.jsonl
+  type: uri_file
+hyperparameters:
+  num_train_epochs: "1"
+  per_device_train_batch_size: "1"
+  learning_rate: "0.00002"
+properties:
+  my_property: "my_value"
+tags:
+  foo_tag: "bar"
+outputs:
+  registered_model:
+    name: "Phi-3-mini-4k-instruct-finetuned-model"
+    type: mlflow_model